{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T07:04:52Z","timestamp":1772780692980,"version":"3.50.1"},"reference-count":45,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T00:00:00Z","timestamp":1699315200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pharmacol."],"abstract":"<jats:p><jats:bold>Introduction:<\/jats:bold> SARS-CoV-2 is a novel coronavirus with highly contagious and has posed a significant threat to global public health. The main protease (M<jats:sup>pro<\/jats:sup>) is a promising target for antiviral drugs against SARS-CoV-2.<\/jats:p><jats:p><jats:bold>Methods:<\/jats:bold> In this study, we have used pharmacophore-based drug design technology to identify potential compounds from drug databases as M<jats:sup>pro<\/jats:sup> inhibitors.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> The procedure involves pharmacophore modeling, validation, and pharmacophore-based virtual screening, which identifies 257 compounds with promising inhibitory activity.<\/jats:p><jats:p><jats:bold>Discussion:<\/jats:bold> Molecular docking and non-bonding interactions between the targeted protein M<jats:sup>pro<\/jats:sup> and compounds showed that ENA482732 was the best compound. These results provided a theoretical foundation for future studies of M<jats:sup>pro<\/jats:sup> inhibitors against SARS-CoV-2.<\/jats:p>","DOI":"10.3389\/fphar.2023.1288363","type":"journal-article","created":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T09:58:01Z","timestamp":1699351081000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Computer-aided drug design for virtual-screening and active-predicting of main protease (Mpro) inhibitors against SARS-CoV-2"],"prefix":"10.3389","volume":"14","author":[{"given":"Renhui","family":"Dai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongwei","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruiling","family":"Su","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2023,11,7]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"105452","DOI":"10.1016\/j.compbiomed.2022.105452","article-title":"Multidimensional in silico strategy for identification of natural polyphenols-based SARS-CoV-2 main protease (M(pro)) inhibitors to unveil a hope against COVID-19","volume":"145","author":"Adem","year":"2022","journal-title":"Comput. 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